Process Analytical Technology Method Research Articles

Proof of a LOD prediction model with orthogonal PAT methods in continuous wet granulation and drying

Real-time monitoring of critical quality attributes, such as residual water in granules after drying which can be determined through loss-on-drying (LOD), during wet granulation and drying is essential in continuous manufacturing. Near-infrared (NIR) spectroscopy has been widely used as process analytical technology (PAT) for in-line LOD monitoring. This study aims to develop and apply a model for predicting the LOD based on process parameters. Additionally, the efficacy of an orthogonal PAT approach using NIR and mass balance (MB) for a vibrating fluidized bed dryer (VFBD) is demonstrated. An in-house-built, cost-effective NIR sensor was utilized for measurements and exhibited good correlation compared to standard method via infrared drying. The combination of NIR and MB, as independent methods, has demonstrated their applicability. A good correlation, with a Pearson r above 0.99, was observed for LOD up to 16 % (w/w). The use of an orthogonal PAT method mitigated the risk of false process adaption. In some experiments where the NIR sensor might have been covered by powder and therefore did not measure accurately, LOD monitoring via MB remained feasible. The developed model effectively predicted LOD or process parameters, resulting in an R2 of 0.882 and a RMSE of 0.475 between predicted and measured LOD using the standard method.

Defining the Range of Calibration Burden: From Full Calibration to Calibration-Free

The expansion of spectroscopic process analytical technology (PAT) within the pharmaceutical industry requires chemometric models to enable spectral interpretation. However, calibration of chemometric models often require an intense time, material, and financial burden which may discourage PAT deployment. The advancement of modeling strategies that reduce this calibration burden necessitates consistent terminology for describing model inputs and overall calibration burden throughout chemometric literature. The goal of this article is to define levels of calibration burden by clarifying dataset classifications and model inputs during PAT method development. The scope of this work is specific to calibration of chemometric models incorporated in PAT methods. Harmonizing the language used in the field of chemometrics to describe calibration burden benefits both pharmaceutical manufacturers and regulatory agencies.

Screening for quality with process analytical technology in a health-system pharmacy: A primer.

The University of Kentucky Drug Quality Study team briefly reviews the growing concerns over pharmaceutical manufacturing quality in the globalized environment, reviews the historical approach by the US Food and Drug Administration (FDA) that prioritizes process over product in enforcing quality with manufacturers, reviews the science of process analytical technology (PAT) such as near-infrared (NIR) spectroscopy, illustrates the use of PAT methods for assessing uniformity and quality in injectable pharmaceuticals, and demonstrates the application of NIR spectroscopy in a health-system pharmacy setting while maintaining current good practice quality guidelines and regulations (cGxP). Given that the current approach to monitoring quality in pharmaceutical manufacturing was developed in the late 1960s at a time when manufacturing was mostly domestic, the current approach prioritizes process over product, and the global footprint of manufacturing is straining federal resources to fulfill their task of monitoring quality, an approach to augment the quality monitoring process has been developed. PAT methodologies are supported by FDA for monitoring quality and offer a fast, low-cost, nondestructive solution. Given that the Accreditation Council for Pharmacy Education has not required qualitative/quantitative analysis and drug assaying in the pharmacy curriculum for several decades, the authors spend time explaining the science behind one of these PAT methodologies, NIR spectroscopy. This primer reviews the application of this technology in the health-system pharmacy setting and the relevant clinical applications. Utilizing PAT methodologies such as NIR spectroscopy, health-system pharmacies can gain insights about whether process controls are in place or lacking in FDA-approved formulations.

Process Analytical Technology for the Production of Parenteral Lipid Emulsions According to Good Manufacturing Practices

The good manufacturing practices (GMP) and process analytical technology (PAT) initiatives of the US Food and Drug Administration, in conjunction with International Council for Harmonisation (ICH) quality guidelines Q8, Q9, and Q10, ensure that manufacturing processes for parenteral formulations meet the requirements of increasingly strict regulations. This involves the selection of suitable process analytics for process integration and aseptic processing. In this article, we discuss the PAT requirements for the GMP-compliant manufacturing of parenteral lipid emulsions, which can be used for clinical nutrition or for the delivery of lipophilic active ingredients. There are risks associated with the manufacturing processes, including the potential for unstable emulsions and the formation of large droplets that can induce embolisms in the patient. Parenteral emulsions are currently monitored offline using a statistical approach. Inline analytics, supplemented by measurements of zeta potential, could minimize the above risks. Laser scanning technology, ultrasound attenuation spectroscopy, and photo-optical sensors combined with image analysis may prove to be useful PAT methods. In the future, these technologies could lead to better process understanding and control, thus improving production efficiency.

Process analytical technology for on-line monitoring of quality attributes during single-use ultrafiltration/diafiltration.

Process analytical technology (PAT) is a fast-growing field within bioprocessing that enables innovation in biological drug manufacturing. This study demonstrates novel PAT methods for monitoring multiple quality attributes simultaneously during the ultrafiltration and diafiltration (UF/DF) process operation, the final step of monoclonal antibody (mAb) purification. Size exclusion chromatography (SEC) methods were developed to measure excipients arginine, histidine, and high molecular weight (HMW) species using a liquid chromatography (LC) system with autosampler for both on-line and at-line PAT modes. The methods were applied in UF/DF studies for the comparison of single-use tangential flow filtration (TFF) cassettes to standard reusable cassettes to achieve very high concentration mAb drug substance (DS) in the order of 100-200 g/L. These case studies demonstrated that single-use TFF cassettes are a functionally equivalent, low-cost alternative to standard reusable cassettes, and that the on-line PAT measurement of purity and excipient concentration was comparable to orthogonal offline methods. These PAT applications using an on-line LC system equipped with onboard sample dilution can become a platform system for monitoring of multiple attributes over a wide dynamic range, a potentially valuable tool for biological drug development and manufacturing.

Artificial neural networks for quantitative online NMR spectroscopy

Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.Graphical abstract

Current and future requirements to industrial analytical infrastructure\u2014part 1: process analytical laboratories

The competitiveness of the chemical and pharmaceutical industry is based on ensuring the required product quality while making optimum use of plants, raw materials, and energy. In this context, effective process control using reliable chemical process analytics secures global competitiveness. The setup of those control strategies often originate in process development but need to be transferable along the whole product life cycle. In this series of two contributions, we want to present a combined view on the future of PAT (process analytical technology), which is projected in smart labs (part 1) and smart sensors (part 2). In laboratories and pilot plants, offline chemical analytical methods are frequently used, where inline methods are also used in production. Here, a transferability from process development to the process in operation would be desirable. This can be obtained by establishing PAT methods for production already during process development or scale-up. However, the current PAT (Bakeev 2005, Org Process Res 19:3–62; Simon et al. 2015, Org Process Res Dev 19:3–62) must become more flexible and smarter. This can be achieved by introducing digitalization-based knowledge management, so that knowledge from product development enables and accelerates the integration of PAT. Conversely, knowledge from the production process will also contribute to product and process development. This contribution describes the future role of the laboratory and develops requirements therefrom. In part 2, we examine the future functionality as well as the ingredients of a smart sensor aiming to eventually fuel full PAT functionality—also within process development or scale-up facilities (Eifert et al. 2020, Anal Bioanal Chem).

Development of an In-Line Near-Infrared Method for Blend Content Uniformity Assessment in a Tablet Feed Frame.

Process analytical technology (PAT) has shown great potential for in-line tableting process monitoring. The study focuses on the development and validation of an in-line near-infrared (NIR) spectroscopic method for the determination of content uniformity of blends in a tablet feed frame. An in-line NIR method was developed after careful evaluation of the impact of potential experimental factors on the robustness and model accuracy and precision. The NIR method was validated according to the principles outlined in International Conference on Harmonization-Q2 for validation of analytical procedures and was demonstrated to be suitable for monitoring blend content for the formulation under evaluation. Reliable measurements of blend homogeneity rely on representative sampling. To reach the appropriate scale of scrutiny for a unit dose, the study assessed factors that influence the effective sample size measured by NIR. Spectral averaging, integration time, and feed frame paddle wheel speed were found to influence the effective sample size measured by the NIR probe. The effective sampling size was also estimated by comparing the distribution of predicted values with the reference values. The development of a robust, in-line PAT method was facilitated by thorough understanding of the sensitivity of PAT sensors to factors affecting pharmaceutical processes and products.

New unique PAT method and instrument for real-time inline size characterization of concentrated, flowing nanosuspensions

With the rise of nanotherapeutics -and nano based products in general-, there has been an increasing need for better understanding and control of nano-particle (NP) synthesis and formulation processes. Size characteristics are often primary, if not critical, quality attributes of nanodispersions. Process Analytical Technology (PAT) tools for inline size characterization during dispersion processing are therefore highly desired. Traditional methods for NP sizing -based on Dynamic Light Scattering (DLS) – are typically ill-suited for direct inline application: (i) typical dispersion turbidities in process conditions often exceed by far the application limits for DLS (ii) agitation/flow typical for process conditions is incompatible with standard DLS and (iii) direct and convenient inline application requires a non-invasive PAT tool giving measurements on process relevant time scales.In this article we describe a new non-invasive PAT instrument – the NanoFlowSizer (patent pending)- which provides continuous, real-time, inline size and PSD characterization of concentrated and/or flowing nanodispersions in process environments. The instrument employs Fourier Domain low coherence interferometry, yielding path length resolved dynamic light scattering data of nanodispersions. Particle size characteristics can be analyzed from these data while effects of flow and/or multiple scattering are simultaneously characterized and accounted for. As first application examples we describe (i) real-time monitoring of NP size characteristics by remote measurement of mono and bi-disperse suspensions at different turbidities in a stirred beaker (ii) real-time monitoring of NP size characteristics using an online sampling loop with a micro-flow cell and (iii) real-time inline monitoring of size characteristics of a pharmaceutical nanoemulsion during industrial pilot scale nano-emulsification and for a pharmaceutical NP suspension during circulation, at flowrates ranging up to ~l/min.

Orthogonal Redundant Monitoring of a New Continuous Fluid-Bed Dryer for Pharmaceutical Processing by Means of Mass and Energy Balance Calculations and Spectroscopic Techniques

In line with the ongoing shift from batch to continuous pharmaceutical production of solid oral dosage forms, a novel continuous fluid-bed dryer was developed. The forced feed nature of the Glatt GPCG2 CM fluid-bed dryer allows continuous, first-in-first-out drying of wet granulate materials based on its compartmentalized, rotating fluidizing chamber. The presented work aims to introduce the dryer’s functionalities in detail, and to demonstrate that the rotating fluid-bed chambers facilitates a stable drying behavior, which ensures robust and repeatable residual moisture contents (loss-on-drying [LOD]) of the discharged granules. Furthermore, a mass and energy balance (MEB) is derived, based on the logged process values of the granulating and drying units. Two independent test experiments demonstrate that precise LOD prediction in real time is achievable by MEB to serve as an orthogonal process analytical technology method to common near-infrared spectroscopy. On average, MEB results differed by 0.36% LOD (absolute) from offline reference analyses, and by 0.61% LOD from predictions made with an in-house available near-infrared spectroscopy method. Furthermore, good correlation between the observed and expected thermal energy loss was found. The derived MEB is solely based on physical principles; hence it is product independent and transferable to other materials that are processed on the described equipment.

Development of near infrared spectroscopic calibration models for in-line determination of low drug concentration, bulk density, and relative specific void volume within a feed frame

This study describes the development of a near infrared (NIR) calibration model for real time determination of drug concentration, powder density, and porosity or relative specific void volume (RSVV) of 3.00%w/w acetaminophen blends within a feed frame. The NIR calibration model was developed from 1.50 to 4.50%w/w of acetaminophen, using a high variability of major excipients (from 12.92 to 81.95%w/w) which facilitates the prediction of powder density and RSVV based on near infrared calibration spectra. The model using second derivative as spectral preprocessing explained the changes related to acetaminophen concentration in the first latent variable. The second latent variable was related to changes in concentration of microcrystalline cellulose and lactose in the powder blends. NIR calibrations were also developed based on the bulk density and RSVV of the powder blends using the same design as the API model, due to the physical properties of the particles and their effects on the NIR spectra. The RSVV was predicted for the independent set blends with an RSEP(%) below 4% with a significantly low bias (0.04 cm3/g) from reference values of 1.33 to 1.58 cm3/g. The bulk density model also exhibited excellent predictions with RSEP(%) below 2.6% and significantly low bias (0.01 g/cm3) from reference values of 0.45 to 0.51 g/cm3. The excellent results obtained show the potential of near infrared spectroscopic measurements within the feed frame for a Process Analytical Technology method to control the critical properties such as tablet mass, hardness and dissolution in batch and continuous manufacturing processes.

A “Large-N” Content Uniformity Process Analytical Technology (PAT) Method for Phenytoin Sodium Tablets

Accurate assessment of tablet content uniformity is critical for narrow therapeutic index drugs such as phenytoin sodium. This work presents a near-infrared (NIR)-based analytical method for rapid prediction of content uniformity based on a large number of phenytoin sodium formulation tablets. Calibration tablets were generated through an integrated experimental design by varying formulation and process parameters, and scale of manufacturing. A partial least squares model for individual tablet content was developed based on tablet NIR spectra. The tablet content was obtained from a modified United States Pharmacopeia phenytoin sodium high-performance liquid chromatography assay method. The partial least squares model with 4 latent variables explained 92% of the composition variability and yielded a root mean square error of prediction of 0.48% w/w. The resultant NIR model successfully assayed the composition of tablets manufactured at the pilot scale. For one such batch, bootstrapping was applied to calculate the confidence intervals on the mean, acceptance value, and relative SD for different sample sizes, n = 10, 30, and 100. As the bootstrap sample size increased, the confidence interval on the mean, acceptance value, and relative SD became narrower and symmetric. Such a ‘large N’ NIR-based process analytical technology method can increase reliability of quality assessments in solid dosage manufacturing.

Method Development and Validation of an Inline Process Analytical Technology Method for Blend Monitoring in the Tablet Feed Frame Using Raman Spectroscopy.

Inline process analytical technology sensors are the key elements to enable continuous manufacturing. They facilitate real-time monitoring of critical quality attributes of both intermediate materials and finished products. The aim of this study was to demonstrate method development and validation for inline and offline calibration strategies to determine the blend content during tablet compression via Raman spectroscopy. An inline principal component regression model was developed from Raman spectra collected in the feed frame. At the same time, an offline study was conducted over a small amount of the calibration blends using an in-house moving powder setup to simulate the environment of the feed frame. The model developed offline was able to predict the active ingredient content after a bias correction and used only a fraction of the material. The offline method can serve as a simple method to facilitate calibration development when the time and access to the press is limited. The study takes into consideration, the necessary components of method development and offers perspectives on the validation of an inline process analytics method. Method testing and validation was performed for the inline process analytical technology method. The established Raman method was demonstrated as suitable for the determination of bulk assay of the active ingredient in powders inside the feed frame for use during batch and continuous manufacturing processes.

Model-based NIR spectroscopy implementation for in-line assay monitoring during a pharmaceutical suspension manufacturing process

The implementation of Process Analytical Technology (PAT) instruments is generally achieved stochastically. Sub-optimal PAT locations could introduce variation in the measurements which is not related to the analyte of interest. For this reason, rational approaches should be considered to establish an optimal sensor placement where relevant measurements are possible and the impact of disturbances is minimized. The aim of this paper is to demonstrate how mechanistic modelling can support appropriate sensor implementation by means of a case study. A PAT method was developed for a bottle filling process of a pharmaceutical formulation with the goal of increasing the yield of the process by gaining process understanding and redefining the endpoint of the process. To ensure proper measurements, an advanced measuring interfacing was assembled. The design of this device was rationalized with the help of a model-based approach using three-dimensional Computational Fluid Dynamics modeling. This allows to maximize the performance of the PAT method and exploit its full benefits.

In-line Fourier-transform infrared spectroscopy as a versatile process analytical technology for preparative protein chromatography

Fourier-transform infrared spectroscopy (FTIR) is a well-established spectroscopic method in the analysis of small molecules and protein secondary structure. However, FTIR is not commonly applied for in-line monitoring of protein chromatography. Here, the potential of in-line FTIR as a process analytical technology (PAT) in downstream processing was investigated in three case studies addressing the limits of currently applied spectroscopic PAT methods. A first case study exploited the secondary structural differences of monoclonal antibodies (mAbs) and lysozyme to selectively quantify the two proteins with partial least squares regression (PLS) giving root mean square errors of cross validation (RMSECV) of 2.42 g/l and 1.67 g/l, respectively. The corresponding Q2 values are 0.92 and, respectively, 0.99, indicating robust models in the calibration range. Second, a process separating lysozyme and PEGylated lysozyme species was monitored giving an estimate of the PEGylation degree of currently eluting species with RMSECV of 2.35 g/l for lysozyme and 1.24 g/l for PEG with Q2 of 0.96 and 0.94, respectively. Finally, Triton X-100 was added to a feed of lysozyme as a typical process-related impurity. It was shown that the species could be selectively quantified from the FTIR 3D field without PLS calibration. In summary, the proposed PAT tool has the potential to be used as a versatile option for monitoring protein chromatography. It may help to achieve a more complete implementation of the PAT initiative by mitigating limitations of currently used techniques.

Raman Spectroscopy for Monitoring the Continuous Crystallization of Carbamazepine

Crystallization has a significant impact on the properties of the active pharmaceutical ingredient (API) since it is the final step in the manufacturing of the drug substance and determines particle size distribution, purity, shape, and polymorphs. Many publications have focused on the implementation of Process Analytical Technology (PAT) tools for monitoring batch and continuous operation; however, a comprehensive method development and validation of Raman spectroscopy to monitor continuous crystallization has not been presented. This work demonstrates the development and validation of a method to monitor the solute concentration of Carbamazepine and quantifies the limit of detection for a metastable polymorphic form. The experiments were based on the cooling crystallization of Carbamazepine to produce the most stable form. The method was validated following the principles described in USP general chapter ⟨1225⟩ validation procedures for analytical methods. The results demonstrate the model can predict the solute concentration with a root-mean-square-error of prediction of 2.46 mg/mL. The repeatability and intermediate precision were evaluated, and the relative standard deviation is below 5%. The limit of detection for the metastable form was determined by monitoring the ratio of characteristic peaks when increasing the percentage of the metastable form in the total amount of crystals in the solution. A significant change in the peak ratio is observed when 22.9% of the crystals are of the metastable form. In addition, this PAT method was used to monitor a continuous run for 10 residence times, in which the system reached a controlled state of operation after six residence times.

On-Line Ion Exchange Liquid Chromatography as a Process Analytical Technology for Monoclonal Antibody Characterization in Continuous Bioprocessing.

Combining process analytical technology (PAT) with continuous production provides a powerful tool to observe and control monoclonal antibody (mAb) fermentation and purification processes. This work demonstrates on-line liquid chromatography (on-line LC) as a PAT tool for monitoring a continuous biologics process and forced degradation studies. Specifically, this work focused on ion exchange chromatography (IEX), which is a critical separation technique to detect charge variants. Product-related impurities, including charge variants, that impact function are classified as critical quality attributes (CQAs). First, we confirmed no significant differences were observed in the charge heterogeneity profile of a mAb through both at-line and on-line sampling and that the on-line method has the ability to rapidly detect changes in protein quality over time. The robustness and versatility of the PAT methods were tested by sampling from two purification locations in a continuous mAb process. The PAT IEX methods used with on-line LC were a weak cation exchange (WCX) separation and a newly developed shorter strong cation exchange (SCX) assay. Both methods provided similar results with the distribution of percent acidic, main, and basic species remaining unchanged over a 2 week period. Second, a forced degradation study showed an increase in acidic species and a decrease in basic species when sampled on-line over 7 days. These applications further strengthen the use of on-line LC to monitor CQAs of a mAb continuously with various PAT IEX analytical methods. Implementation of on-line IEX will enable faster decision making during process development and could potentially be applied to control in biomanufacturing.

Calibration Transfer of a Quantitative Transmission Raman PLS Model: Direct Transfer vs. Global Modeling

Global regulatory agencies have encouraged the use of process analytical technology (PAT) to assure quality in the pharmaceutical industry. A frequently cited obstacle to the implementation of spectroscopy-based PAT methods is the difficulty associated with directly transferring calibration models between PAT instruments. The goal of this study was to compare model transfer strategies for method transfer between two transmission Raman spectroscopy (TRS) instruments. The calibration and test samples were pharmaceutical compacts of acetaminophen and excipients. The experimental design was a 3 factor by 5 level circumscribed central composite design of active pharmaceutical ingredient, lactose, and microcrystalline cellulose concentrations. The calibration and test data were collected using two instruments. Quantitative models were constructed using partial least squares regression. Global calibration modeling and direct model transfer were compared to evaluate opportunities for situations involving method transfer, calibration update, and line extension. Models were compared using a t test-based method to evaluate performance statistics. Statistical analysis demonstrated equivalent performance of the global modeling and direct transfer methods. This work demonstrated that a quantitative transmission Raman model could be directly transferred across instruments, thus avoiding the challenges and resources necessary when creating global models.

In-line Raman spectroscopic monitoring and feedback control of a continuous twin-screw pharmaceutical powder blending and tableting process

The integration of Process Analytical Technology (PAT) initiative into the continuous production of pharmaceuticals is indispensable for reliable production. The present paper reports the implementation of in-line Raman spectroscopy in a continuous blending and tableting process of a three-component model pharmaceutical system, containing caffeine as model active pharmaceutical ingredient (API), glucose as model excipient and magnesium stearate as lubricant. The real-time analysis of API content, blend homogeneity, and tablet content uniformity was performed using a Partial Least Squares (PLS) quantitative method. The in-line Raman spectroscopic monitoring showed that the continuous blender was capable of producing blends with high homogeneity, and technological malfunctions can be detected by the proposed PAT method. The Raman spectroscopy-based feedback control of the API feeder was also established, creating a ‘Process Analytically Controlled Technology’ (PACT), which guarantees the required API content in the produced blend. This is, to the best of the authors’ knowledge, the first ever application of Raman-spectroscopy in continuous blending and the first Raman-based feedback control in the formulation technology of solid pharmaceuticals.

Application of Process Analytical Technology for Monitoring Freeze-Drying of an Amorphous Protein Formulation: Use of Complementary Tools for Real-Time Product Temperature Measurements and Endpoint Detection

Process analytical technology (PAT) and quality by design have gained importance in all areas of pharmaceutical development and manufacturing. One important method for monitoring of critical product attributes and process optimization in laboratory scale freeze-drying is manometric temperature measurement (MTM). A drawback of this innovative technology is that problems are encountered when processing high-concentrated amorphous materials, particularly protein formulations. In this study, a model solution of bovine serum albumin and sucrose was lyophilized at both conservative and aggressive primary drying conditions. Different temperature sensors were employed to monitor product temperatures. The residual moisture content at primary drying endpoints as indicated by temperature sensors and batch PAT methods was quantified from extracted sample vials. The data from temperature probes were then used to recalculate critical product parameters, and the results were compared with MTM data. The drying endpoints indicated by the temperature sensors were not suitable for endpoint indication, in contrast to the batch methods endpoints. The accuracy of MTM Pice data was found to be influenced by water reabsorption. Recalculation of Rp and Pice values based on data from temperature sensors and weighed vials was possible. Overall, extensive information about critical product parameters could be obtained using data from complementary PAT tools.